#!/usr/bin/python
#coding:utf-8
'''
OneClassSvm异常检测算法
'''
  
import numpy as np  
import matplotlib.pyplot as plt  
import matplotlib.font_manager  
from sklearn import svm  
  
xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))  
# Generate train data  
X = 0.3 * np.random.randn(100, 2)  
X_train = np.r_[X + 2, X - 2]  
# Generate some regular novel observations  
X = 0.3 * np.random.randn(20, 2)  
X_test = np.r_[X + 2, X - 2]  
# Generate some abnormal novel observations  
X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))  
  
# fit the model  
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)  
clf.fit(X_train)  
y_pred_train = clf.predict(X_train)  
y_pred_test = clf.predict(X_test)  
y_pred_outliers = clf.predict(X_outliers)  
n_error_train = y_pred_train[y_pred_train == -1].size  
n_error_test = y_pred_test[y_pred_test == -1].size  
n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size  
  
# plot the line, the points, and the nearest vectors to the plane  
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])  
Z = Z.reshape(xx.shape)  
  
plt.title("Novelty Detection")  
plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)  #绘制异常样本的区域  
a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')  #绘制正常样本和异常样本的边界  
plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')   #绘制正常样本的区域  
s = 40  
b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white', s=s, edgecolors='k')  
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='blueviolet', s=s,  
                 edgecolors='k')  
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='gold', s=s,  
                edgecolors='k')  
plt.axis('tight')  
plt.xlim((-5, 5))  
plt.ylim((-5, 5))  
plt.legend([a.collections[0], b1, b2, c],  
           ["learned frontier", "training observations",  
            "new regular observations", "new abnormal observations"],  
           loc="upper left",  
           prop=matplotlib.font_manager.FontProperties(size=11))  
plt.xlabel(  
    "error train: %d/200 ; errors novel regular: %d/40 ; "  
    "errors novel abnormal: %d/40"  
    % (n_error_train, n_error_test, n_error_outliers))  
plt.show() 